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Abstract Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow. 

While considerable knowledge exists about the enzymes pivotal for C₄photosynthesis, much less is known about thecis-regulation important for specifying their expression in distinct cell types. Here, we use single-cell-indexed ATAC-seq to identify cell-type-specific accessible chromatin regions (ACRs) associated with C₄enzymes for five different grass species. This study spans four C₄species, covering three distinct photosynthetic subtypes:Zea maysandSorghum bicolor(NADP-dependent malic enzyme),Panicum miliaceum(NAD-dependent malic enzyme),Urochloa fusca(phosphoenolpyruvate carboxykinase), along with the C₃outgroupOryza sativa. We studied thecis-regulatory landscape of enzymes essential across all C₄species and those unique to C₄subtypes, measuring cell-type-specific biases for C₄enzymes using chromatin accessibility data. Integrating these data with phylogenetics revealed diverse co-option of gene family members between species, showcasing the various paths of C₄evolution. Besides promoter proximal ACRs, we found that, on average, C₄genes have two to three distal cell-type-specific ACRs, highlighting the complexity and divergent nature of C₄evolution. Examining the evolutionary history of these cell-type-specific ACRs revealed a spectrum of conserved and novel ACRs, even among closely related species, indicating ongoing evolution ofcis-regulation at these C₄loci. This study illuminates the dynamic and complex nature ofcis-regulatory elements evolution in C₄photosynthesis, particularly highlighting the intricatecis-regulatory evolution of key loci. Our findings offer a valuable resource for future investigations, potentially aiding in the optimization of C₃crop performance under changing climatic conditions. 

The ease and throughput of single-cell genomics have steadily improved, and its current trajectory suggests that surveying single-cell populations will become routine. We discuss the merger of quantitative genetics with single-cell genomics and emphasize how this synergizes with advantages intrinsic to plants. Single-cell population genomics provides increased detection resolution when mapping variants that control molecular traits, including gene expression or chromatin accessibility. Additionally, single-cell population genomics reveals the cell types in which variants act and, when combined with organism-level phenotype measurements, unveils which cellular contexts impact higher-order traits. Emerging technologies, notably multiomics, can facilitate the measurement of both genetic changes and genomic traits in single cells, enabling single-cell genetic experiments. The implementation of single-cell genetics will advance the investigation of the genetic architecture of complex molecular traits and provide new experimental paradigms to study eukaryotic genetics. 

Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called single-cell combinatorial fluidic indexing ATAC-sequencing (scifi-ATAC-seq), which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using a widely commercialized microfluidics platform (10X Genomics). With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples in a single emulsion reaction can be indexed, representing a ~20-fold increase in throughput compared to the standard 10X Genomics workflow.